Inherent Parallelism and Speedup Estimation of Sequential Programs

Abstract

Although several automated Parallel Conversion solutions are available, very few have attempted, to provide proper estimates of the available Inherent Parallelism and expected Parallel Speedup. CALIPER which is the outcome of this research work is a parallel performance estimation technology that can fill this void. High level language structures such as Functions, Loops, Conditions, etc which ease program development, can be a hindrance for effective performance analysis. We refer to these program structures as the Program Shape. As a preparatory step, CALIPER attempts to remove these shape related hindrances, an activity we refer to as Program Shape Flattening. Programs are also characterized by dependences that exist between different instructions and impose an upper limit on the parallel conversion gains. For parallel estimation, we first group instructions that share dependences, and add them to a class we refer to as Dependence Class or Parallel Class. While instructions belonging to a class run sequentially, the classes themselves run in parallel. Parallel runtime, is now the runtime of the class that runs the longest. We report performance estimates of parallel conversion as two metrics. The inherent parallelism in the program is reported, as Maximum Available Parallelism (MAP) and the speedup after conversion as Speedup After Parallelization (SAP).